An introduction to geometric and topological methods to analyze large scale biological data; includes statistics and genomic applications.

Genomes from bacteria to eukaryotes are intricately organized by the mutual interplay between the three-dimensional (3D) folding of their genome and their functional cell activities. In this dissertation, we propose exhaustive computational and statistical approaches to analyze chromosome conformation capture (CCC) data to investigate the 3D structure of the genome at both the level of CCC interaction counts between genomic loci and that of the 3D physical reconstruction structure. In this work, we use the yeast Saccharomyces cerevisiae (S. cerevisiae) as a model system. Our first result identifies the Rabl configuration, an evolutionary conserved feature of the 3D nuclear organization, characterized by the clustering of centromeres on one side of the nuclear envelope and the telomeres at the antipodal side, as an essential player in the simplification of the entanglement of chromatin fibers. In our approach, we introduced a new geometrical invariant termed the linking proportion that can capture the entanglement between pairs of chromosomes. Next, we showcase a novel approach of statistical topology whereby agreement between chromatin configuration reconstructions, which includes reproducibility of chromatin con- figurations and evaluation of chromatin reconstruction algorithms, can be evaluated. Our proposed approach makes use of the linking proportion together with statistical methods in inference to reach the important conclusion that the multidimensional scaling methods fails to preserve chromosomal topology. Finally, we present Smooth3D, a novel approach of inferring the 3D genome configuration structure from the CCC interaction counts based on cubic spline approximation. Smooth3D produces the 3D chromosomal trajectory from the CCC interactions counts via B-spline curve fitting using a least-squares algorithm. Our method estimates both the parameter of the transfer counts to distance function and the 3D chromosomal trajectory.

A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

The massive research effort known as the Human Genome Project is an attempt to record the sequence of the three trillion nucleotides that make up the human genome and to identify individual genes within this sequence. While the basic effort is of course a biological one, the description and classification of sequences also lend themselves naturally to mathematical and statistical modeling. This short textbook on the mathematics of genome analysis presents a brief description of several ways in which mathematics and statistics are being used in genome analysis and sequencing. It will be of interest not only to students but also to professional mathematicians curious about the subject.

The second edition of this book adds eight new contributors to reflect a modern cutting edge approach to genomics. It contains the newest research results on genomic analysis and modeling using state-of-the-art methods from engineering, statistics, and genomics. These tools and models are then applied to real biological and clinical problems. The book’s original seventeen chapters are also updated to provide new initiatives and directions.

Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.